d2l-pytorch-notebooks/chapter_recurrent-neural-networks/rnn-scratch.ipynb

Recurrent Neural Network Implementation from Scratch

🏷️sec_rnn-scratch

We are now ready to implement an RNN from scratch. In particular, we will train this RNN to function as a character-level language model (see :numref:sec_rnn) and train it on a corpus consisting of the entire text of H. G. Wells' The Time Machine, following the data processing steps outlined in :numref:sec_text-sequence. We start by loading the dataset.

In [1]:

%matplotlib inline
import math
import torch
from torch import nn
from torch.nn import functional as F
from d2l import torch as d2l

RNN Model

We begin by defining a class to implement the RNN model (:numref:subsec_rnn_w_hidden_states). Note that the number of hidden units num_hiddens is a tunable hyperparameter.

In [2]:

class RNNScratch(d2l.Module):  #@save
    """The RNN model implemented from scratch."""
    def __init__(self, num_inputs, num_hiddens, sigma=0.01):
        super().__init__()
        self.save_hyperparameters()
        self.W_xh = nn.Parameter(
            torch.randn(num_inputs, num_hiddens) * sigma)
        self.W_hh = nn.Parameter(
            torch.randn(num_hiddens, num_hiddens) * sigma)
        self.b_h = nn.Parameter(torch.zeros(num_hiddens))

[The forward method below defines how to compute the output and hidden state at any time step, given the current input and the state of the model at the previous time step.] Note that the RNN model loops through the outermost dimension of inputs, updating the hidden state one time step at a time. The model here uses a \tanh activation function (:numref:subsec_tanh).

In [3]:

@d2l.add_to_class(RNNScratch)  #@save
def forward(self, inputs, state=None):
    if state is None:
        # Initial state with shape: (batch_size, num_hiddens)
        state = torch.zeros((inputs.shape[1], self.num_hiddens),
                          device=inputs.device)
    else:
        state, = state
    outputs = []
    for X in inputs:  # Shape of inputs: (num_steps, batch_size, num_inputs)
        state = torch.tanh(torch.matmul(X, self.W_xh) +
                         torch.matmul(state, self.W_hh) + self.b_h)
        outputs.append(state)
    return outputs, state

We can feed a minibatch of input sequences into an RNN model as follows.

In [4]:

batch_size, num_inputs, num_hiddens, num_steps = 2, 16, 32, 100
rnn = RNNScratch(num_inputs, num_hiddens)
X = torch.ones((num_steps, batch_size, num_inputs))
outputs, state = rnn(X)

Let's check whether the RNN model produces results of the correct shapes to ensure that the dimensionality of the hidden state remains unchanged.

In [5]:

def check_len(a, n):  #@save
    """Check the length of a list."""
    assert len(a) == n, f'list\'s length {len(a)} != expected length {n}'

def check_shape(a, shape):  #@save
    """Check the shape of a tensor."""
    assert a.shape == shape, \
            f'tensor\'s shape {a.shape} != expected shape {shape}'

check_len(outputs, num_steps)
check_shape(outputs[0], (batch_size, num_hiddens))
check_shape(state, (batch_size, num_hiddens))

RNN-Based Language Model

The following RNNLMScratch class defines an RNN-based language model, where we pass in our RNN via the rnn argument of the __init__ method. When training language models, the inputs and outputs are from the same vocabulary. Hence, they have the same dimension, which is equal to the vocabulary size. Note that we use perplexity to evaluate the model. As discussed in :numref:subsec_perplexity, this ensures that sequences of different length are comparable.

In [6]:

class RNNLMScratch(d2l.Classifier):  #@save
    """The RNN-based language model implemented from scratch."""
    def __init__(self, rnn, vocab_size, lr=0.01):
        super().__init__()
        self.save_hyperparameters()
        self.init_params()

    def init_params(self):
        self.W_hq = nn.Parameter(
            torch.randn(
                self.rnn.num_hiddens, self.vocab_size) * self.rnn.sigma)
        self.b_q = nn.Parameter(torch.zeros(self.vocab_size))

    def training_step(self, batch):
        l = self.loss(self(*batch[:-1]), batch[-1])
        self.plot('ppl', torch.exp(l), train=True)
        return l

    def validation_step(self, batch):
        l = self.loss(self(*batch[:-1]), batch[-1])
        self.plot('ppl', torch.exp(l), train=False)

[One-Hot Encoding]

Recall that each token is represented by a numerical index indicating the position in the vocabulary of the corresponding word/character/word piece. You might be tempted to build a neural network with a single input node (at each time step), where the index could be fed in as a scalar value. This works when we are dealing with numerical inputs like price or temperature, where any two values sufficiently close together should be treated similarly. But this does not quite make sense. The 45^{\textrm{th}} and 46^{\textrm{th}} words in our vocabulary happen to be "their" and "said", whose meanings are not remotely similar.

When dealing with such categorical data, the most common strategy is to represent each item by a one-hot encoding (recall from :numref:subsec_classification-problem). A one-hot encoding is a vector whose length is given by the size of the vocabulary N, where all entries are set to 0, except for the entry corresponding to our token, which is set to 1. For example, if the vocabulary had five elements, then the one-hot vectors corresponding to indices 0 and 2 would be the following.

In [7]:

F.one_hot(torch.tensor([0, 2]), 5)

Out [7]:

tensor([[1, 0, 0, 0, 0],
        [0, 0, 1, 0, 0]])

(The minibatches that we sample at each iteration will take the shape (batch size, number of time steps). Once representing each input as a one-hot vector, we can think of each minibatch as a three-dimensional tensor, where the length along the third axis is given by the vocabulary size (len(vocab)).) We often transpose the input so that we will obtain an output of shape (number of time steps, batch size, vocabulary size). This will allow us to loop more conveniently through the outermost dimension for updating hidden states of a minibatch, time step by time step (e.g., in the above forward method).

In [8]:

@d2l.add_to_class(RNNLMScratch)  #@save
def one_hot(self, X):
    # Output shape: (num_steps, batch_size, vocab_size)
    return F.one_hot(X.T, self.vocab_size).type(torch.float32)

Transforming RNN Outputs

The language model uses a fully connected output layer to transform RNN outputs into token predictions at each time step.

In [9]:

@d2l.add_to_class(RNNLMScratch)  #@save
def output_layer(self, rnn_outputs):
    outputs = [torch.matmul(H, self.W_hq) + self.b_q for H in rnn_outputs]
    return torch.stack(outputs, 1)

@d2l.add_to_class(RNNLMScratch)  #@save
def forward(self, X, state=None):
    embs = self.one_hot(X)
    rnn_outputs, _ = self.rnn(embs, state)
    return self.output_layer(rnn_outputs)

Let's [check whether the forward computation produces outputs with the correct shape.]

In [10]:

model = RNNLMScratch(rnn, num_inputs)
outputs = model(torch.ones((batch_size, num_steps), dtype=torch.int64))
check_shape(outputs, (batch_size, num_steps, num_inputs))

[Gradient Clipping]

While you are already used to thinking of neural networks as "deep" in the sense that many layers separate the input and output even within a single time step, the length of the sequence introduces a new notion of depth. In addition to the passing through the network in the input-to-output direction, inputs at the first time step must pass through a chain of T layers along the time steps in order to influence the output of the model at the final time step. Taking the backwards view, in each iteration, we backpropagate gradients through time, resulting in a chain of matrix-products of length \mathcal{O}(T). As mentioned in :numref:sec_numerical_stability, this can result in numerical instability, causing the gradients either to explode or vanish, depending on the properties of the weight matrices.

Dealing with vanishing and exploding gradients is a fundamental problem when designing RNNs and has inspired some of the biggest advances in modern neural network architectures. In the next chapter, we will talk about specialized architectures that were designed in hopes of mitigating the vanishing gradient problem. However, even modern RNNs often suffer from exploding gradients. One inelegant but ubiquitous solution is to simply clip the gradients forcing the resulting "clipped" gradients to take smaller values.

Generally speaking, when optimizing some objective by gradient descent, we iteratively update the parameter of interest, say a vector \mathbf{x}, but pushing it in the direction of the negative gradient \mathbf{g} (in stochastic gradient descent, we calculate this gradient on a randomly sampled minibatch). For example, with learning rate \eta > 0, each update takes the form \mathbf{x} \gets \mathbf{x} - \eta \mathbf{g}. Let's further assume that the objective function f is sufficiently smooth. Formally, we say that the objective is Lipschitz continuous with constant L, meaning that for any \mathbf{x} and \mathbf{y}, we have

|f(\mathbf{x}) - f(\mathbf{y})| \leq L \|\mathbf{x} - \mathbf{y}\|.

As you can see, when we update the parameter vector by subtracting \eta \mathbf{g}, the change in the value of the objective depends on the learning rate, the norm of the gradient and L as follows:

|f(\mathbf{x}) - f(\mathbf{x} - \eta\mathbf{g})| \leq L \eta\|\mathbf{g}\|.

In other words, the objective cannot change by more than L \eta \|\mathbf{g}\|. Having a small value for this upper bound might be viewed as good or bad. On the downside, we are limiting the speed at which we can reduce the value of the objective. On the bright side, this limits by just how much we can go wrong in any one gradient step.

When we say that gradients explode, we mean that \|\mathbf{g}\| becomes excessively large. In this worst case, we might do so much damage in a single gradient step that we could undo all of the progress made over the course of thousands of training iterations. When gradients can be so large, neural network training often diverges, failing to reduce the value of the objective. At other times, training eventually converges but is unstable owing to massive spikes in the loss.

One way to limit the size of L \eta \|\mathbf{g}\| is to shrink the learning rate \eta to tiny values. This has the advantage that we do not bias the updates. But what if we only rarely get large gradients? This drastic move slows down our progress at all steps, just to deal with the rare exploding gradient events. A popular alternative is to adopt a gradient clipping heuristic projecting the gradients \mathbf{g} onto a ball of some given radius \theta as follows:

($\mathbf{g} \leftarrow \min\left(1, \frac{\theta}{\|\mathbf{g}\|}\right) \mathbf{g}.$)

This ensures that the gradient norm never exceeds \theta and that the updated gradient is entirely aligned with the original direction of \mathbf{g}. It also has the desirable side-effect of limiting the influence any given minibatch (and within it any given sample) can exert on the parameter vector. This bestows a certain degree of robustness to the model. To be clear, it is a hack. Gradient clipping means that we are not always following the true gradient and it is hard to reason analytically about the possible side effects. However, it is a very useful hack, and is widely adopted in RNN implementations in most deep learning frameworks.

Below we define a method to clip gradients, which is invoked by the fit_epoch method of the d2l.Trainer class (see :numref:sec_linear_scratch). Note that when computing the gradient norm, we are concatenating all model parameters, treating them as a single giant parameter vector.

In [11]:

@d2l.add_to_class(d2l.Trainer)  #@save
def clip_gradients(self, grad_clip_val, model):
    params = [p for p in model.parameters() if p.requires_grad]
    norm = torch.sqrt(sum(torch.sum((p.grad ** 2)) for p in params))
    if norm > grad_clip_val:
        for param in params:
            param.grad[:] *= grad_clip_val / norm

Training

Using The Time Machine dataset (data), we train a character-level language model (model) based on the RNN (rnn) implemented from scratch. Note that we first calculate the gradients, then clip them, and finally update the model parameters using the clipped gradients.

In [12]:

data = d2l.TimeMachine(batch_size=1024, num_steps=32)
rnn = RNNScratch(num_inputs=len(data.vocab), num_hiddens=32)
model = RNNLMScratch(rnn, vocab_size=len(data.vocab), lr=1)
trainer = d2l.Trainer(max_epochs=100, gradient_clip_val=1, num_gpus=1)
trainer.fit(model, data)

Decoding

Once a language model has been learned, we can use it not only to predict the next token but to continue predicting each subsequent one, treating the previously predicted token as though it were the next in the input. Sometimes we will just want to generate text as though we were starting at the beginning of a document. However, it is often useful to condition the language model on a user-supplied prefix. For example, if we were developing an autocomplete feature for a search engine or to assist users in writing emails, we would want to feed in what they had written so far (the prefix), and then generate a likely continuation.

[The following predict method generates a continuation, one character at a time, after ingesting a user-provided prefix]. When looping through the characters in prefix, we keep passing the hidden state to the next time step but do not generate any output. This is called the warm-up period. After ingesting the prefix, we are now ready to begin emitting the subsequent characters, each of which will be fed back into the model as the input at the next time step.

In [13]:

@d2l.add_to_class(RNNLMScratch)  #@save
def predict(self, prefix, num_preds, vocab, device=None):
    state, outputs = None, [vocab[prefix[0]]]
    for i in range(len(prefix) + num_preds - 1):
        X = torch.tensor([[outputs[-1]]], device=device)
        embs = self.one_hot(X)
        rnn_outputs, state = self.rnn(embs, state)
        if i < len(prefix) - 1:  # Warm-up period
            outputs.append(vocab[prefix[i + 1]])
        else:  # Predict num_preds steps
            Y = self.output_layer(rnn_outputs)
            outputs.append(int(Y.argmax(axis=2).reshape(1)))
    return ''.join([vocab.idx_to_token[i] for i in outputs])

In the following, we specify the prefix and have it generate 20 additional characters.

In [14]:

model.predict('it has', 20, data.vocab, d2l.try_gpu())

Out [14]:

'it has in the the the the '

While implementing the above RNN model from scratch is instructive, it is not convenient. In the next section, we will see how to leverage deep learning frameworks to whip up RNNs using standard architectures, and to reap performance gains by relying on highly optimized library functions.

Summary

We can train RNN-based language models to generate text following the user-provided text prefix. A simple RNN language model consists of input encoding, RNN modeling, and output generation. During training, gradient clipping can mitigate the problem of exploding gradients but does not address the problem of vanishing gradients. In the experiment, we implemented a simple RNN language model and trained it with gradient clipping on sequences of text, tokenized at the character level. By conditioning on a prefix, we can use a language model to generate likely continuations, which proves useful in many applications, e.g., autocomplete features.

Exercises

1. Does the implemented language model predict the next token based on all the past tokens up to the very first token in The Time Machine?
2. Which hyperparameter controls the length of history used for prediction?
3. Show that one-hot encoding is equivalent to picking a different embedding for each object.
4. Adjust the hyperparameters (e.g., number of epochs, number of hidden units, number of time steps in a minibatch, and learning rate) to improve the perplexity. How low can you go while sticking with this simple architecture?
5. Replace one-hot encoding with learnable embeddings. Does this lead to better performance?
6. Conduct an experiment to determine how well this language model trained on The Time Machine works on other books by H. G. Wells, e.g., The War of the Worlds.
7. Conduct another experiment to evaluate the perplexity of this model on books written by other authors.
8. Modify the prediction method so as to use sampling rather than picking the most likely next character.
   • What happens?
   • Bias the model towards more likely outputs, e.g., by sampling from q(x_t \mid x_{t-1}, \ldots, x_1) \propto P(x_t \mid x_{t-1}, \ldots, x_1)^\alpha for \alpha > 1.
9. Run the code in this section without clipping the gradient. What happens?
10. Replace the activation function used in this section with ReLU and repeat the experiments in this section. Do we still need gradient clipping? Why?

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